Quantitative trading is the usage of mathematical models or algorithms to create trading strategies and trade them. Quant trading is usually employed by large institutional traders or hedge funds who employ large teams of PhDs and engineers. Historically the quantitative trading field has been very secretive and ideas which work tend to be guarded very closely by the firms but in the last few years the growth of openly available datasets and access to compute i.e ( in the form of GPUs and cloud) has made quant trading accessible to a larger audience. Each of the above steps involve lot of research and trial and error to get right. Quant trading is a complex field and requires careful and detailed study to be successful. The following are 10 such books which can help one get started on their Quant journey.

Most traders or investors in the financial market dream of having a system that automatically trades for them without the need for them to do anything else trading related. While no such system truly exists, algorithmic trading comes very close. Based on a recent market report, the global algorithmic market valued at $10.3 thousand in 2018 is anticipated to witness a CAGR of 10% over a forecast period (2022-2027). The demand for a fast, reliable, and profitable system is spearheading the growth of algorithmic trading. However, despite the availability of various materials, a beginner with a non-technical background might find it very difficult to follow a systematic approach to learning algorithmic trading.

Source code (with copious amounts of comments) is attached as a resource with all the code-alongs. Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce.

Are you familiar with self-driving cars? These things would not be possible without the help of Machine Learning--the study of pattern recognition and prediction within the field of computer science. This course is taught by Stanford-educated, Silicon Valley experts that have decades of direct experience under their belts. They will teach you, in the simplest way possible (and with major visual techniques), to put Machine Learning and Python into action. With these skills under your belt, your programming skills will take a whole new level of power.

Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning.

Prerequisites: Working knowledge of Python is necessary if you want to run the source code that is provided. Basic knowledge of machine learning, especially ML classification techniques, would be helpful but it's not mandatory. Taught by a Stanford-educated, ex-Googler and an IIT, IIM - educated ex-Flipkart lead analyst. This team has decades of practical experience in quant trading, analytics and e-commerce. Completely Practical: This course has just enough theory to get you started with both Quant Trading and Machine Learning.